Hyperspherical cluster based distributed anomaly detection in wireless sensor networks

• Detecting anomalies in data is challenging on resource constrained networks.
• We propose a distributed algorithm using hyperspherical cluster based data models.
• The scheme is capable of identifying global anomalies at an individual node level.
• Comparable detection accuracy with significant reduction in communication overhead.
• Implemented and demonstrated on a real wireless sensor network test-bed.

This article describes a distributed hyperspherical cluster based algorithm for identifying anomalies in measurements from a wireless sensor network, and an implementation on a real wireless sensor network testbed. The communication overhead incurred in the network is minimised by clustering sensor measurements and merging clusters before sending a compact description of the clusters to other nodes. An evaluation on several real and synthetic datasets demonstrates that the distributed hyperspherical cluster-based scheme achieves comparable detection accuracy with a significant reduction in communication overhead compared to a centralised scheme, where all the sensor node measurements are communicated to a central node for processing.